• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.510-513
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• 2002

대부분의 이진화 알고리즘은 임계치를 결정하기 위하여 히스토그램을 사용하여 밝기분포를 분석한다. 배경과 물체의 명도차이가 큰 경우에는 분할을 위해 양봉(bimodal) 히스토그램으로 표현하여 최적의 임계치를 찾기 위해 히스토그램 골짜기(valley)를 선택하는 것만으로도 양호한 임계치 결과를 얻을수 있으나, 배경과 물체의 밝기 차이가 크지 않거나 밝기 분포가 양봉 특성을 보이지 않을 때는 히스토그램 분석만으로 적절한 임계치를 얻기 어렵다. 그리고 한 영상에서는 넓은 영역에 걸쳐 명암도 변화가 일어나고 다양한 유형의 물체가 포함되어 있으므로 스케치 특징점 유무를 판별하는 임계치의 결정에는 애매 모호함이 존재한다. 따라서 본 논문에서는 영상에 대해 삼각형 타입의 소속함수를 적용하여 임계치를 동적으로 설정하고 영상을 이진화하는 방법을 제안한다. 제안된 퍼지 이진화 방법은 평균 밝기 값을 기준으로 가장 어두운 픽셀 값과 가장 밝은 픽셀값의 거리를 계산하여 밝기의 조정률을 구하여 최소 밝기값과 최대 밝기 값을 설정하고 삼각형의 소속 함수에 적용한다. 소속 함수에 적용된 소속도를 a-cut 을 적용하여 영상을 이진화한다. 다양한 영상에 적용한 결과, 기존의 이진화 방법보다 제안된 퍼지 이진화 방법이 효율적인 것을 알 수 있었다.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.21-27
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• 2006

This paper is for the segmentation and volume calculation of the white matter and gray matter from brain MRI. We segment white matter, gray matter and CSF from the Brain image in the normal and abnormal person, and calculate the volume of segmented tissue. In this paper, we present a new method of extracting white matter, gray matter and CSF and calculation its volume from MR images for brain. And we have developed the determining method of threshold that can extract white matter and gray matter from MR image for brain through the analysis of gray values represented by ratio of each component. We proposed the calculation method of volume for white matter and gray matter by using number of extracted pixels in each slice. This algorithm input CSF/Head volume ratio and age of patient and calculates discriminant value through discriminant expression, classifies normal and abnormal using calculated discriminant value. As a result, we could blow that white matter and gray matter volume decrease and CSF volume increase as we grow gold.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.22 no.8
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• pp.1152-1163
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• 1998

In the present work, the image processing technique for measurement of drop sizes has been improved. Firstly, the local processing concept was adopted in addition to the global processing technique to take account of non-uniformity of the illumination intensity ; thereby, basically, the measurement error can be reduced. Also, the unfocussed image of drops can be eliminated more precisely since the elimination process is based on the local normalized contrast. Secondly the algorithms to process the partially detected or overlapped drop images and the non-spherical drop images were developed. Finally, the improved algorithm was tested by using an artificially prepared image-frame, where the partial or overlapped particles and the non-spherical particles are mixed with the normal spherical ones (with their true size-distributions known a priori). The results showed that both the recognition rate of the number of particles and the measurement accuracy were improved prominently.

• Journal of IKEEE
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• v.19 no.2
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• pp.193-200
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• 2015

In this paper, we propose the radiation measuring algorithm and the device composition using CMOS image sensor. The radiation measuring algorithm using CMOS image sensor is based on the radiation particle distinguishing algorithm projected to the CMOS image sensor and accumulated and average number of pixels of the radiation particles projected to dozens of images per second with CMOS image sensor. The radiation particle distinguishing algorithm projected to the CMOS image sensor measures the radiation particle images by dividing them into R, G and B and adjusting the threshold value that distinguishes light intensity and background from the particle of each image. The radiation measuring algorithm measures radiation with accumulated and average number of radiation particles projected to dozens of images per second with CMOS image sensor according to the preset cycle. The hardware devices to verify the suggested algorithm consists of CMOS image sensor and image signal processor part, control part, power circuit part and display part. The test result of radiation measurement using the suggested CMOS image sensor is as follows. First, using the low-cost CMOS image sensor to measure radiation particles generated similar characteristics to that from measurement with expensive GM Tube. Second, using the low-cost CMOS image sensor to measure radiation presented largely similar characteristics to the linear characteristics of expensive GM Tube.